Seminars

Title: ShEx Learning from Typed Graphs

Abstract: In knowledge graphs, schemas are becoming a new asset to describe the organization of data. The new world-leading format Shex is becoming a de-facto standard in the industry that allows defining flexible and powerful schemas.

In this context, the inference of schemas can become a solution to provide shex expressions that describe already existing data. Typically, the inference starts from untyped graphs. However, these tasks appears to be more complex than expected in general, and is possible only for subclasses of Shex.

The inference of schemas from typed graph gives a baseline for those algorithms. Its comprehension allows to understand better the underlying difficulties of the task. It presents already unexpected difficulties.

We present an algorithm that infers Shex-defined schemas from fully typed graphs. We also present some encountered difficulties, as well as the limitations of the approach.